---
title: "Does personal relevance attenuate citizens' persuasion by government branding? Evidence from a survey experiment on air-pollution policy"
author: '**Saar Alon-Barkat**'
date: " "
always_allow_html: yes
output:
  html_document:
    theme: flatly
  pdf_document:
    toc: yes
  word_document: default
link-citations: yes
bibliography: phd_paper_2.bib
csl: https://raw.githubusercontent.com/citation-style-language/styles/master/apa.csl
urlcolor: blue
---

<br>

Draft, last edited at `r Sys.Date()`.

```{r set-global-options, echo = FALSE}
knitr::opts_chunk$set(eval = TRUE, 
                      echo = FALSE, 
                      message=FALSE,
                      warning = FALSE,
                      cache = FALSE)

```



```{r , include=FALSE, echo=FALSE}
#load("SVIVA_R_ENV.RData")
source("C:/SAAR/UNIVERSITY/R/SVIVA/code/experiment 2/SVIVA_exp2_dm_03.R")


```



```{r silent-packages}
library(knitr)
library(tidyverse)
library(stargazer)
library(car)
library(broom)
library(kableExtra)
library(ggthemes)
library(lmtest)
library(sandwich)
library(ggpubr)
library(effsize)
library(emmeans)
```


```{r}
N_raw = nrow(SVIVA2_raw_00)
N_0=SVIVA2_00%>%nrow()
N_1=SVIVA2_01%>%nrow()

filter_IP = N_0-(SVIVA2_00 %>%
  distinct(IP,.keep_all=TRUE)%>%nrow())
filter_age = N_0-(SVIVA2_00 %>%
  filter(!(AGE%in% 1:17))%>% nrow())
filter_IMC = N_0-(SVIVA2_00 %>%
  filter(IMC==1)%>% nrow())
filter_time = N_0-(SVIVA2_00 %>%
  filter(TIMER_total>=3,TIMER_total<=30)%>%nrow())

N_1_haifa = filter(SVIVA2_01,AREA==1)%>%nrow()
N_1_center = filter(SVIVA2_01,AREA==0)%>%nrow()



```


```{r}
#Figures and tables
table.x=0
figure.x=0
```

<br>

```{r}

sd.air <- SVIVA2_01_comb%>%summarise(sd(TRUST_air_INDEX,na.rm=T))%>%max()
sd.air.haifa <- SVIVA2_01_comb %>% filter(AREA_center==0)%>%summarise(sd(TRUST_air_INDEX,na.rm=T))%>%max()

sd.waste <- SVIVA2_01_comb%>%summarise(sd(TRUST_waste_INDEX,na.rm=T))%>%max()

```



```{r}
mod_air_null = lm(TRUST_air_INDEX~1,data=SVIVA2_01_comb)

mod_air_1.1 = update(mod_air_null,. ~ .+
                       SYMBOL_t+
                       INFORMATION_weak,
                     data=SVIVA2_01_comb)
mod_air_1.2 = update(mod_air_1.1,. ~ .+AREA_center)
mod_air_1.3 = update(mod_air_1.1,. ~ .+AREA_center*(SYMBOL_t+INFORMATION_weak))

mod_air_1.4 = update(mod_air_1.3,. ~ .+
                      GENDER+
                       AGE+
                       GOV_TRUST+
                       IDEOLOGY+
                       EDUCATION+
                       INCOME+
                       CHILDREN_young)

mod_air_1.5 = update(mod_air_1.3,. ~ .,data=SVIVA2_01_comb_air_first)


## Adjust standard errors & F statistic
mod_air_1.1.robust_se    <- sqrt(diag(vcovHC(mod_air_1.1, type = "HC1")))
mod_air_1.1.wald_results <- waldtest(mod_air_1.1, vcov = vcovHC(mod_air_1.1, type = "HC1"))

mod_air_1.2.robust_se    <- sqrt(diag(vcovHC(mod_air_1.2, type = "HC1")))
mod_air_1.2.wald_results <- waldtest(mod_air_1.2, vcov = vcovHC(mod_air_1.2, type = "HC1"))

mod_air_1.3.robust_se    <- sqrt(diag(vcovHC(mod_air_1.3, type = "HC1")))
mod_air_1.3.wald_results <- waldtest(mod_air_1.3, vcov = vcovHC(mod_air_1.3, type = "HC1"))

mod_air_1.4.robust_se    <- sqrt(diag(vcovHC(mod_air_1.4, type = "HC1")))
mod_air_1.4.wald_results <- waldtest(mod_air_1.4, vcov = vcovHC(mod_air_1.4, type = "HC1"))

mod_air_1.5.robust_se    <- sqrt(diag(vcovHC(mod_air_1.5, type = "HC1")))
mod_air_1.5.wald_results <- waldtest(mod_air_1.5, vcov = vcovHC(mod_air_1.5, type = "HC1"))

```



```{r}

mod_waste_null = lm(TRUST_waste_INDEX~1,data=SVIVA2_01_comb)
mod_waste_1.1 = update(mod_waste_null,. ~ .+
                       SYMBOL_t+
                       INFORMATION_weak,
                     data=SVIVA2_01_comb)
mod_waste_1.2 = update(mod_waste_1.1,. ~ .+AREA_center)
mod_waste_1.3 = update(mod_waste_1.1,. ~ .+AREA_center*(SYMBOL_t+INFORMATION_weak))
mod_waste_1.4 = update(mod_waste_1.3,. ~ .+
                      GENDER+
                       AGE+
                       GOV_TRUST+
                       IDEOLOGY+
                       EDUCATION+
                       INCOME+
                       CHILDREN_young)
SVIVA2_01_comb_recycling_first <- SVIVA2_01_comb %>% filter(AIR_order==2)
mod_waste_1.5 = update(mod_waste_1.3,. ~ .,data=SVIVA2_01_comb_recycling_first)

## Adjust standard errors & F statistic
mod_waste_1.1.robust_se    <- sqrt(diag(vcovHC(mod_waste_1.1, type = "HC1")))
mod_waste_1.1.wald_results <- waldtest(mod_waste_1.1, vcov = vcovHC(mod_waste_1.1, type = "HC1"))

mod_waste_1.2.robust_se    <- sqrt(diag(vcovHC(mod_waste_1.2, type = "HC1")))
mod_waste_1.2.wald_results <- waldtest(mod_waste_1.2, vcov = vcovHC(mod_waste_1.2, type = "HC1"))

mod_waste_1.3.robust_se    <- sqrt(diag(vcovHC(mod_waste_1.3, type = "HC1")))
mod_waste_1.3.wald_results <- waldtest(mod_waste_1.3, vcov = vcovHC(mod_waste_1.3, type = "HC1"))

mod_waste_1.4.robust_se    <- sqrt(diag(vcovHC(mod_waste_1.4, type = "HC1")))
mod_waste_1.4.wald_results <- waldtest(mod_waste_1.4, vcov = vcovHC(mod_waste_1.4, type = "HC1"))

mod_waste_1.5.robust_se    <- sqrt(diag(vcovHC(mod_waste_1.5, type = "HC1")))
mod_waste_1.5.wald_results <- waldtest(mod_waste_1.5, vcov = vcovHC(mod_waste_1.4, type = "HC1"))

```


```{r}

mod_relevance_null = lm(TRUST_air_INDEX~1,data=SVIVA2_01_comb.haifa)
mod_relevance_1.1 = update(mod_relevance_null,. ~ .+
                             SYMBOL_t+
                   INFORMATION_weak+
                   RELEVANCE_exp)
mod_relevance_1.2 = update(mod_relevance_1.1,. ~ .+RELEVANCE_exp*(SYMBOL_t+INFORMATION_weak))
mod_relevance_1.3 = update(mod_relevance_1.2,. ~ .+
                   GENDER+
                   AGE+
                   GOV_TRUST+
                   IDEOLOGY+
                   EDUCATION+
                   INCOME+
                   CHILDREN_young)


mod_relevance_1.4 = update(mod_relevance_1.2,. ~ .,data=SVIVA2_01_comb.haifa_air_first)

## Adjust standard errors & F statistic
mod_relevance_1.1.robust_se    <- sqrt(diag(vcovHC(mod_relevance_1.1, type = "HC1")))
mod_relevance_1.1.wald_results <- waldtest(mod_relevance_1.1, vcov = vcovHC(mod_relevance_1.1, type = "HC1"))

mod_relevance_1.2.robust_se    <- sqrt(diag(vcovHC(mod_relevance_1.2, type = "HC1")))
mod_relevance_1.2.wald_results <- waldtest(mod_relevance_1.2, vcov = vcovHC(mod_relevance_1.2, type = "HC1"))

mod_relevance_1.3.robust_se    <- sqrt(diag(vcovHC(mod_relevance_1.3, type = "HC1")))
mod_relevance_1.3.wald_results <- waldtest(mod_relevance_1.3, vcov = vcovHC(mod_relevance_1.3, type = "HC1"))

mod_relevance_1.4.robust_se    <- sqrt(diag(vcovHC(mod_relevance_1.4, type = "HC1")))
mod_relevance_1.4.wald_results <- waldtest(mod_relevance_1.4, vcov = vcovHC(mod_relevance_1.3, type = "HC1"))


SVIVA2_01_comb.haifa <- SVIVA2_01_comb.haifa %>% 
  mutate(RELEVANCE_exp.r = Recode(RELEVANCE_exp,"0=1;1=0"),
         SYMBOL_t.r = Recode(SYMBOL_t,"0=2;2=0"))

mod_relevance_1.2.r = update(mod_relevance_null,. ~ .+RELEVANCE_exp.r*(SYMBOL_t+INFORMATION_weak))

mod_relevance_1.2.r.robust_se <- sqrt(diag(vcovHC(mod_relevance_1.2.r, type = "HC1")))

diff5 <- (mod_relevance_1.2.r %>% tidy() %>% filter(term=="SYMBOL_t1") %>% select(estimate) %>% max(.)) %>% abs() %>% round(3)
se5 <- (mod_relevance_1.2.r.robust_se %>% tidy() %>% filter(names=="SYMBOL_t1") %>% select(x)) %>% max()


mod_relevance_1.2.r.r = update(mod_relevance_null,. ~ .+RELEVANCE_exp.r*(SYMBOL_t.r+INFORMATION_weak))

tab.t1 <- SVIVA2_01_comb.haifa %>% 
  group_by(RELEVANCE_exp,
           INFORMATION_weak) %>% 
  summarise(n = n(),
            mean = mean(TRUST_air_INDEX,na.rm = T),
            sd = sd(TRUST_air_INDEX,na.rm = T),
            q1 = quantile(TRUST_air_INDEX,0.25,na.rm=T),
            median = quantile(TRUST_air_INDEX,0.5,na.rm=T),
            q3 = quantile(TRUST_air_INDEX,0.75,na.rm=T)) %>% 
  mutate(se = sd/sqrt(n)) %>% 
  mutate(ci.low = mean - 1.96*se,
         ci.high = mean + 1.96*se)

mod_symbols_relevance_aircontent = lm(TRUST_air_INDEX ~ SYMBOL_t,SVIVA2_01_haifa.relevance.aircontent)

mod_symbols_relevance_aircontent.r = lm(TRUST_air_INDEX ~ SYMBOL_t.r,SVIVA2_01_haifa.relevance.aircontent)

main.symbols.relevance.aircontent <- emmeans::emmeans(mod_symbols_relevance_aircontent, specs = pairwise ~ SYMBOL_t)

t.symbols.relevance.aircontent.no <- t.test(TRUST_air_INDEX~SYMBOL_t,data=SVIVA2_01_haifa.relevance.aircontent %>% filter(SYMBOL_t!=2))
t.symbols.relevance.aircontent.fake <- t.test(TRUST_air_INDEX~SYMBOL_t,data=SVIVA2_01_haifa.relevance.aircontent %>% filter(SYMBOL_t!=0))

```

 

```{r}
mod_closedness_null = lm(TRUST_air_INDEX~+
                   GOV_TRUST+
                   IDEOLOGY+
                   EDUCATION+
                   INCOME+
                     AGE,data=SVIVA2_01_comb.haifa)

mod_closedness_1.1 = update(mod_closedness_null,. ~ .+
                             SYMBOL_t+
                   INFORMATION_weak+
                   close.industrial.area+
                     CHILDREN_young)

mod_closedness_1.2 = update(mod_closedness_1.1,. ~ .
                            +close.industrial.area*(SYMBOL_t+INFORMATION_weak)+
                              CHILDREN_young*(SYMBOL_t+INFORMATION_weak)+
                              GENDER*(SYMBOL_t+INFORMATION_weak))

mod_closedness_1.3 = update(mod_closedness_1.2,. ~ .,data=SVIVA2_01_comb.haifa_air_first)


## Adjust standard errors & F statistic
mod_closedness_1.1.robust_se    <- sqrt(diag(vcovHC(mod_closedness_1.1, type = "HC1")))
mod_closedness_1.1.wald_results <- waldtest(mod_closedness_1.1, vcov = vcovHC(mod_closedness_1.1, type = "HC1"))

mod_closedness_1.2.robust_se    <- sqrt(diag(vcovHC(mod_closedness_1.2, type = "HC1")))
mod_closedness_1.2.wald_results <- waldtest(mod_closedness_1.2, vcov = vcovHC(mod_closedness_1.2, type = "HC1"))

mod_closedness_1.3.robust_se    <- sqrt(diag(vcovHC(mod_closedness_1.3, type = "HC1")))
mod_closedness_1.3.wald_results <- waldtest(mod_closedness_1.3, vcov = vcovHC(mod_closedness_1.3, type = "HC1"))

```


# Figures and tables

```{r}
#Figures and tables
table.x=0
figure.x=0
```

<br><br>

`r table.x=table.x+1`**Table `r table.x`: Balancing of experimental groups across areas**

<br>

```{r}

#labels of vars
vars.lab = c(
         "Gender (Woman=1)",
         "Age",
         "Trust in government ministries",
         "Political Ideology (10 = extreme left)",
         "Education",
         "Income (5=high)",
         "Parents",
         "Parents of young children (under 12)",
         "Interest in environmental issues",
         "Haifa Bay air-pollution policy",
         "Recycling policy"
)

vars <- c(var1 = "GENDER", 
          var2 ="AGE",
          var3 ="GOV_TRUST",
          var4 = "IDEOLOGY", 
          var5 ="EDUCATION",
          var6 ="INCOME",
          var7 = "CHILDREN", 
          var8 ="CHILDREN_young",
          var9 ="ENVIRONMENT_INTEREST",
          var91 ="TRUST_air_INDEX",
          var92 ="TRUST_waste_INDEX")

t1.all <- SVIVA2_01 %>% 
  select(AREA_center,
         !!vars) %>% 
  gather(key = variable, value = value,-AREA_center) %>% 
  group_by(AREA_center,variable) %>% 
  summarise(mean.var = mean(value,na.rm = T) %>% round(3),
            sd.var = sd(value,na.rm = T) %>% round(3)) %>% 
  mutate(all = str_c(mean.var, " (",sd.var,")")) %>% 
  as.data.frame() %>% 
  mutate(variable = rep(vars,2)) %>% 
  select(-mean.var,-sd.var)
  
t1.symbol <- SVIVA2_01 %>%
  mutate(symbols.t = Recode(SYMBOL,"0='no';1='fake';2='real'")) %>% 
  select(AREA_center,
         symbols.t,
         !!vars) %>%
  gather(key = variable, value = value, -symbols.t,-AREA_center) %>% 
  group_by(symbols.t, variable,AREA_center) %>% 
  summarise(value = list(value)) %>% 
  spread(symbols.t, value) %>% 
  group_by(AREA_center,variable) %>% 
  
  mutate(mean_no = round(mean(unlist(no),na.rm = T),3),
         mean_fake = round(mean(unlist(fake),na.rm = T),3),
         mean_real = round(mean(unlist(real),na.rm = T),3),
         sd_no = round(sd(unlist(no),na.rm = T),3),
         sd_fake = round(sd(unlist(fake),na.rm = T),3),
         sd_real = round(sd(unlist(real),na.rm = T),3)) %>%
  mutate(no = str_c(mean_no, " (",sd_no,")"),
         fake = str_c(mean_fake, " (",sd_fake,")"),
         real = str_c(mean_real, " (",sd_real,")")) %>% 
  select(AREA_center,
         variable,
         no,
         fake,
         real) %>% 
  as.data.frame() %>% 
  arrange(AREA_center,variable)
  
t2.symbol <- SVIVA2_01 %>% 
  select(AREA_center,
         SYMBOL,
         !!vars) %>% 
  gather(key = variable, value = value, -SYMBOL,-AREA_center) %>% 
  group_by(variable,AREA_center) %>% 
  do(tidy(aov(value~SYMBOL, data=.))) %>% 
  as.data.frame() %>% 
  filter(term=="SYMBOL") %>% 
  select(AREA_center,
         variable,
         statistic) %>% 
  mutate_at(c("statistic"), funs(round(.,3)))%>% 
  arrange(AREA_center,variable)

t3.symbol <- cbind(t1.symbol,
                   t2.symbol %>% select(statistic)) 

t1.info <- SVIVA2_01 %>% 
  mutate(info.t = ifelse(INFORMATION_air==1,"air_strong","air_weak")) %>% 
  select(AREA_center,
         info.t,
         !!vars) %>%
  gather(key = variable, value = value, -info.t, -AREA_center) %>% 
  group_by(info.t, variable,AREA_center) %>% 
  summarise(value = list(value)) %>% 
  spread(info.t, value) %>% 
  group_by(AREA_center,variable) %>% 
  
  mutate(mean_strong = round(mean(unlist(air_strong),na.rm = T),3),
         mean_weak = round(mean(unlist(air_weak),na.rm = T),3),
         sd_strong = round(sd(unlist(air_strong),na.rm = T),3),
         sd_weak = round(sd(unlist(air_weak),na.rm = T),3),
         t_value.info = round(t.test(unlist(air_strong), unlist(air_weak))$statistic,3)) %>% 
  mutate(air_strong = str_c(mean_strong, " (",sd_strong,")"),
         air_weak = str_c(mean_weak, " (",sd_weak,")")) %>% 
  select(AREA_center,
         variable,
         air_strong,
         air_weak,
         t_value.info) %>% 
  as.data.frame() %>% 
  arrange(AREA_center,variable)  

t1.relevance <- SVIVA2_01 %>%
  mutate(relevance.t = ifelse(RELEVANCE_exp==1,"treatment","control")) %>% 
  select(AREA_center,
         relevance.t,
         !!vars) %>%
  select(-var9) %>%
  mutate(var9 = SVIVA2_01$SYMBOL_bi) %>% 
  gather(key = variable, value = value, -relevance.t, -AREA_center) %>% 
  group_by(relevance.t, variable,AREA_center) %>% 
  summarise(value = list(value)) %>% 
  spread(relevance.t, value) %>% 
  group_by(AREA_center,variable) %>% 
  
  mutate(mean_treatment = round(mean(unlist(treatment),na.rm = T),3),
         mean_control = round(mean(unlist(control),na.rm = T),3),
         sd_treatment = round(sd(unlist(treatment),na.rm = T),3),
         sd_control = round(sd(unlist(control),na.rm = T),3),
         t_value.relevance = round(t.test(unlist(treatment), unlist(control))$statistic,3)) %>% 
  mutate(treatment = str_c(mean_treatment, " (",sd_treatment,")"),
         control = str_c(mean_control, " (",sd_control,")")) %>% 
  select(AREA_center,
         variable,
         treatment,
         control,
         t_value.relevance) %>% 
  as.data.frame() %>% 
  arrange(AREA_center,variable) 

t1.relevance[t1.relevance$variable=="var9",-c(1,2)]="-"



t3.haifa <- t1.all %>% 
  filter(AREA_center==0) %>% 
  select(-AREA_center) %>% 
  cbind(t3.symbol  %>% 
          filter(AREA_center==0) %>% 
          select(-AREA_center,-variable)) %>% 
  cbind(t1.info %>% 
          filter(AREA_center==0) %>% 
          select(-AREA_center,-variable)) %>%
  cbind(t1.relevance %>% 
          filter(AREA_center==0) %>% 
          select(-AREA_center,-variable)) %>% 
  mutate(variable = vars.lab)


t3.center <- t1.all %>% 
  filter(AREA_center==1) %>% 
  select(-AREA_center) %>% 
  cbind(t3.symbol  %>% 
          filter(AREA_center==1) %>% 
          select(-AREA_center,-variable)) %>% 
  cbind(t1.info %>% 
          filter(AREA_center==1) %>% 
          select(-AREA_center,-variable)) %>%
  cbind(t1.relevance %>% 
          filter(AREA_center==1) %>% 
          select(-AREA_center,-variable)) %>%  
  mutate(variable = vars.lab)

```


**Haifa Bay**
```{r}

t3.haifa %>%
  filter(str_detect(variable,"policy")==F) %>%
  kable(col.names = c("",
                    str_c("All sample (n=",SVIVA2_01_haifa %>% nrow(),")"),
                    str_c("No symbols (n=",SVIVA2_01_haifa %>% filter(SYMBOL==0) %>%  nrow(),")"),
                    str_c("Fake symbols (n=",SVIVA2_01_haifa %>% filter(SYMBOL==1) %>%  nrow(),")"),
                    str_c("Real symbols (n=",SVIVA2_01_haifa %>% filter(SYMBOL==2) %>%  nrow(),")"),
                    "f-test",
                    str_c("Strong Air-pollution (Weak recycling) (n=",SVIVA2_01_haifa %>% filter(INFORMATION_air==1) %>% nrow(),")"),
                    str_c("Weak air-pollution (Strong recycling) (n=",SVIVA2_01_haifa %>% filter(INFORMATION_air==0) %>% nrow(),")"),
                     "t-test",
                    str_c("Treatment (n=",SVIVA2_01_haifa %>% filter(RELEVANCE_exp==1) %>% nrow(),")"),
                    str_c("Control (n=",SVIVA2_01_haifa %>% filter(RELEVANCE_exp==0) %>% nrow(),")"),
                    "t-test"))%>%
  kable_styling(bootstrap_options = c("condensed"),
                full_width = F,
                position = "left",
                font_size = 9) %>%
  add_header_above(c(" " = 2, 
                     "Symbols groups" = 4,  
                     "Information groups" = 3, 
                     "Relevance groups" = 3))
```
<font size="0.5">

*Notes*: 
Table entries are means with SDs in parentheses. 

</font>

<br>
**Center**
```{r}

t3.center %>%
    filter(str_detect(variable,"policy")==F) %>%
    kable(col.names = c("",
                    str_c("All sample (n=",SVIVA2_01_center %>% nrow(),")"),
                    str_c("No symbols (n=",SVIVA2_01_center %>% filter(SYMBOL==0) %>%  nrow(),")"),
                    str_c("Fake symbols (n=",SVIVA2_01_center %>% filter(SYMBOL==1) %>%  nrow(),")"),
                    str_c("Real symbols (n=",SVIVA2_01_center %>% filter(SYMBOL==2) %>%  nrow(),")"),
                    "f-test",
                    str_c("Strong Air-pollution (Weak recycling) (n=",SVIVA2_01_center %>% filter(INFORMATION_air==1) %>% nrow(),")"),
                    str_c("Weak air-pollution (Strong recycling) (n=",SVIVA2_01_center %>% filter(INFORMATION_air==0) %>% nrow(),")"),
                     "t-test",
                    str_c("Treatment (n=",SVIVA2_01_center %>% filter(RELEVANCE_exp==1) %>% nrow(),")"),
                    str_c("Control (n=",SVIVA2_01_center %>% filter(RELEVANCE_exp==0) %>% nrow(),")"),
                    "t-test"))%>%
  kable_styling(bootstrap_options = c("condensed"),
                full_width = F,
                position = "left",
                font_size = 9) %>%
  add_header_above(c(" " = 2, 
                     "Symbols groups" = 4,  
                     "Information groups" = 3, 
                     "Relevance groups" = 3))
```
<font size="0.5">

*Notes*: 
Table entries are means with SDs in parentheses. 

</font>

<br><br>

`r figure.x=figure.x+1`**Figure `r figure.x`: Perceived personal relevance of policy issues across areas**

```{r, fig.width=8,fig.height=4}
SVIVA2_01 %>%
  select(AREA_names,
         RELEVANCE_air_obs,
         RELEVANCE_waste_obs) %>% 
  gather(key = policy, value = relevance, -AREA_names) %>%
  mutate(policy = Recode(policy,"'RELEVANCE_air_obs'='Haifa Bay air-pollution policy';'RELEVANCE_waste_obs'='Recycling policy'")) %>% 
ggplot(aes(AREA_names,relevance))+ 
  theme_tufte()+
  geom_boxplot(width=0.25,color="gray70")+
  stat_summary(fun.data = mean_cl_normal, geom = "errorbar",
               fun.args = list(mult = 1.96),
               width=0.05,size=0.5) + 
  stat_summary(fun.y = mean, geom = "point", size=1.6)+
  facet_grid(~policy)+
  xlab("")+
  scale_y_continuous(name = "Perceived personal relevance",limits=c(1,7),breaks = seq(1,7,by=1))
```
<font size="0.5">

*Note*: Error bars represent 95% confidence intervals.

</font>

<br><br>

`r figure.x=figure.x+1`**Figure `r figure.x`**

```{r, fig.width=8, fig.height=4}
p1 <- SVIVA2_01_comb %>%
ggplot(aes(SYMBOL_n,TRUST_air_INDEX))+ 
  theme_tufte()+
  geom_boxplot(width=0.25,color="gray70")+
  stat_summary(fun.data = mean_cl_normal, geom = "errorbar",
               fun.args = list(mult = 1.96),
               width=0.05,size=0.5) + 
  stat_summary(fun.y = mean, geom = "point", size=1.6)+
  stat_summary(fun.y = mean, geom = "line", size=0.6, aes(group = 1))+
  scale_x_discrete(name = "",labels = c("No symbols","Fake symbols","Real symbols"))+
  scale_y_continuous(name = "Trust in Haifa Bay air-pollution policy",limits=c(1,7),breaks = seq(1,7,by=1))+
  ggtitle("Symbols")

p2 <- SVIVA2_01_comb %>%
ggplot(aes(factor(INFORMATION_weak),TRUST_air_INDEX))+ 
  theme_tufte()+
  geom_boxplot(width=0.25,color="gray70")+
  stat_summary(fun.data = mean_cl_normal, geom = "errorbar",
               fun.args = list(mult = 1.96),
               width=0.05,size=0.5) + 
  stat_summary(fun.y = mean, geom = "point", size=1.6)+
  stat_summary(fun.y = mean, geom = "line", size=0.6, aes(group = 1))+
  scale_x_discrete(name = "",labels = c("Strong policy","Weak policy"))+
  scale_y_continuous(name = "",limits=c(1,7),breaks = seq(1,7,by=1))+
  ggtitle("Information")

p3 <- SVIVA2_01_comb %>%
ggplot(aes(factor(AREA_center),TRUST_air_INDEX))+ 
  theme_tufte()+
  geom_boxplot(width=0.25,color="gray70")+
  stat_summary(fun.data = mean_cl_normal, geom = "errorbar",
               fun.args = list(mult = 1.96),
               width=0.05,size=0.5) + 
  stat_summary(fun.y = mean, geom = "point", size=1.6)+
  stat_summary(fun.y = mean, geom = "line", size=0.6, aes(group = 1))+
  scale_x_discrete(name = "",labels = c("Haifa Bay","Center"))+
  scale_y_continuous(name = "",limits=c(1,7),breaks = seq(1,7,by=1))+
  ggtitle("Areas")

ggarrange(p1,p2,p3,
          ncol = 3,nrow = 1,
          widths = c(3,2,2))
```
<font size="0.5">

*Note*: Error bars represent 95% confidence intervals.

</font>

<br><br>

`r figure.x=figure.x+1`**Figure `r figure.x`: The effect of symbolic elements on trust across areas**

```{r, fig.width=8}
p1 <- SVIVA2_01_comb %>%
  filter(policy=="TRUST_air_INDEX") %>% 
ggplot(aes(factor(SYMBOL_n),trust,shape=factor(AREA_names)))+ 
  theme_tufte()+
  #geom_boxplot(width=0.25,color="gray70")+
  stat_summary(fun.data = mean_cl_normal, geom = "errorbar",
               fun.args = list(mult = 1.96),
               width=0.1,size=0.5,position=position_dodge(0.1),color="gray70") + 
  stat_summary(fun.y = mean, geom = "point", size=1.6,position=position_dodge(0.1))+
  stat_summary(fun.y = mean, geom = "line", size=0.6, aes(group = AREA_names,linetype=AREA_names),position=position_dodge(0.1),alpha=0.8)+
  scale_shape_manual(guide=F, values=c(19,1))+
  scale_linetype_manual(guide=F, values=c(1,4))+
  scale_x_discrete(name="",labels=c("No symbols","Fake symbols","Real symbols"))+
  scale_y_continuous(name = "Trust in Haifa Bay air-pollution policy",breaks = seq(1,7,by=0.5))+
  ggtitle("Symbols")


p2 <- SVIVA2_01_comb %>%
  filter(policy=="TRUST_air_INDEX") %>% 
ggplot(aes(factor(INFORMATION_weak),trust,shape=factor(AREA_names)))+ 
  theme_tufte()+
  #geom_boxplot(width=0.25,color="gray70")+
  stat_summary(fun.data = mean_cl_normal, geom = "errorbar",
               fun.args = list(mult = 1.96),
               width=0.1,size=0.5,position=position_dodge(0.1),color="gray70") + 
  stat_summary(fun.y = mean, geom = "point", size=1.6,position=position_dodge(0.1))+
  stat_summary(fun.y = mean, geom = "line", size=0.6, aes(group = AREA_names,linetype=AREA_names),position=position_dodge(0.1),alpha=0.8)+
  scale_shape_manual(name="Area",values=c(19,1))+
  scale_linetype_manual(name="Area",values=c(1,4))+
  scale_x_discrete(name="",labels=c("Strong policy","Weak policy"))+
  scale_y_continuous(name = "",breaks = seq(1,7,by=0.5))+
  ggtitle("Information")

ggarrange(p1,p2,
          ncol = 2,nrow = 1,
          widths = c(4,5))
```

<font size="0.5">

*Note*: Error bars represent 95% confidence intervals.

</font>

<br><br>

`r table.x=table.x+1`**Table `r table.x`: Regression analyses – Trust in Haifa Bay air-pollution policy.**

```{r,results="asis"}
control.vars <- c("GENDER",
                  "AGE",
                  "GOV_TRUST",
                   "IDEOLOGY",
                   "EDUCATION",
                   "INCOME",
                   "CHILDREN",
                   "CHILDREN_young")

stargazer(mod_air_1.1,mod_air_null,
          mod_air_1.2,mod_air_null,
          mod_air_1.3, mod_air_null,
          mod_air_1.4, mod_air_null,
          type = "html",
          se  = list(mod_air_1.1.robust_se,NULL,
                     mod_air_1.2.robust_se,NULL,
                     mod_air_1.3.robust_se,NULL,
                     mod_air_1.4.robust_se,NULL),
          style = "apsr",
          report = "vcsp",
          omit.stat = c("rsq","ser", "f"),
          initial.zero = FALSE,
          column.labels   = str_c("(1.",1:4,")"),
          column.separate = c(2,2,2,2),
          dep.var.labels="Trust in Haifa Bay air-pollution policy",
          omit = control.vars,
          covariate.labels=c("Real symbols",
                             "Fake-symbols",
                             "Weak Information",
                             "Area (0=Haifa Bay; 1=center)",
                             "Area x Real symbols",
                             "Area x Fake symbols",
                             "Area x Weak Information"),
          p.auto = F,
          add.lines = list(
                           c("Controls","No","","No","","No","","Yes",""),
                           c("F Statistic", 
                             mod_air_1.1.wald_results[2,3] %>% round(2),NA,
                             mod_air_1.2.wald_results[2,3] %>% round(2),NA,
                             mod_air_1.3.wald_results[2,3] %>% round(2),NA,
                             mod_air_1.4.wald_results[2,3] %>% round(2),NA)),
          notes = "*Notes*: In all regression tables, entries are nonstandardized OLS-regression coefficients. Robust standard errors are in parentheses and *p-values* (two-tailed) are reported. The reference category for the symbols manipulation conditions is the control (no symbols).",
          notes.append = FALSE)



```

<br><br>


`r table.x=table.x+1`**Table `r table.x`: Regression analyses – trust in recycling policy.**

```{r,results="asis"}
control.vars <- c("GENDER",
                  "AGE",
                  "GOV_TRUST",
                   "IDEOLOGY",
                   "EDUCATION",
                   "INCOME",
                   "CHILDREN_young")



stargazer(mod_waste_1.1,mod_waste_null,
          mod_waste_1.2,mod_waste_null,
          mod_waste_1.3, mod_waste_null,
          mod_waste_1.4, mod_waste_null,
          type = "html",
          se  = list(mod_waste_1.1.robust_se,NULL,
                     mod_waste_1.2.robust_se,NULL,
                     mod_waste_1.3.robust_se,NULL,
                     mod_waste_1.4.robust_se,NULL),
          style = "apsr",
          report = "vcsp",
          omit.stat = c("rsq","ser", "f"),
          initial.zero = FALSE,
          column.labels   = str_c("(2.",1:4,")"),
          column.separate = c(2,2,2,2,2),
          dep.var.labels="Trust in recycling policy",
          omit = control.vars,
          covariate.labels=c("Real symbols",
                             "Fake-symbols",
                             "Weak Information",
                             "Area (0=Haifa Bay; 1=center)",
                             "Area x Real symbols",
                             "Area x Fake symbols",
                             "Area x Weak Information"),
          p.auto = F,
          add.lines = list(
                           c("Controls","No","","No","","No","","Yes",""),
                           c("F Statistic", 
                             mod_waste_1.1.wald_results[2,3] %>% round(2),NA,
                             mod_waste_1.2.wald_results[2,3] %>% round(2),NA,
                             mod_waste_1.3.wald_results[2,3] %>% round(2),NA,
                             mod_waste_1.4.wald_results[2,3] %>% round(2),NA)),
          notes = "",
          notes.append = FALSE)

```

<br><br>

`r table.x=table.x+1`**Table `r table.x`: Regression analyses – experimental manipulation of perceived personal relevance.**

```{r,results="asis"}
control.vars <- c("GENDER",
                  "AGE",
                  "GOV_TRUST",
                   "IDEOLOGY",
                   "EDUCATION",
                   "INCOME",
                   "CHILDREN_young")


stargazer(mod_relevance_1.1,mod_relevance_null,
          mod_relevance_1.2,mod_relevance_null,
          mod_relevance_1.3, mod_relevance_null,
          type = "html",
          se  = list(mod_relevance_1.1.robust_se,NULL,
                     mod_relevance_1.2.robust_se,NULL,
                     mod_relevance_1.3.robust_se,NULL),
          style = "apsr",
          report = "vcsp",
          omit.stat = c("rsq","ser", "f"),
          initial.zero = FALSE,
          column.labels   = str_c("(3.",1:4,")"),
          column.separate = c(2,2,2),
          dep.var.labels="Trust in Haifa Bay air-pollution policy",
          omit = control.vars,
          covariate.labels=c("Real symbols",
                             "Fake-symbols",
                             "Weak Information",
                             "Relevance treatment",
                             "Relevance treatment x Real symbols",
                             "Relevance treatment x Fake symbols",
                             "Relevance treatment x Weak Information"),
          p.auto = F,
          add.lines = list(
                           c("Controls","No","","No","","Yes",""),
                           c("F Statistic", 
                             mod_relevance_1.1.wald_results[2,3] %>% round(2),NA,
                             mod_relevance_1.2.wald_results[2,3] %>% round(2),NA,
                             mod_relevance_1.3.wald_results[2,3] %>% round(2),NA)),
          notes = "",
          notes.append = FALSE)
```

<br><br>


<br>











